Summary

Traditional recommendation systems suggest results based on data collected from
users' actions. Many of the newer information retrieval (IR) systems incorporate social search or collective search signals as an extension to
standard term-based retrieval algorithms. Systems based on social or collaborative search methods, however, do not consider when, how, and to what extent such support could help or hurt their
users' search performance. In
this poster we pro-pose a novel approach of selective algorithmic mediation capable of identifying when a user should be aided by a collaborator and to what extent such help could enhance search success. We demonstrate
the applicability and benefits of our approach through simulations using a pseudo-collaboration method on the log data of individual users and pairs of users gathered during a laboratory study with 131 participants. The results
show that our approach can improve the search performance of both individual searchers and others collaborating intentionally by identifying and recommending regions in search processes with best chance of improvements,
thus increasing the likelihood that users find more useful information with less
effort.